Evidence map›Paper›PMID 42505466›Full record

ArticleBiosensors2026

Expanding Biolayer Interferometry Applications: Enhanced Accuracy, Precision, and Sensitivity in Residual Biomolecule Detection and Quantitation of Bispecifics and AAV Viral Particles.

Stuart Knowling, Kirsty McBain, David Apiyo

Abstract read
In one paragraph

Article in Biosensors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Stuart KnowlingSartorius Bioanalytical Instruments, 47661 Fremont Blvd, Fremont, CA 94538, USA.
Kirsty McBainSartorius UK Ltd., Grantham Close, York Way, Royston SG8 5WY, UK.
David ApiyoSartorius Bioanalytical Instruments, 47661 Fremont Blvd, Fremont, CA 94538, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biolayer Interferometry (BLI) has traditionally been used for characterization of protein-protein interactions (PPI) with proteins, such as antibodies and their antigens, through kinetic and quantitation assays. Limitations, for example in sensitivity and the availability of established assay formats, have restricted its adoption across other analytical applications. This article highlights three case studies which demonstrate the expansion of BLI into novel applications, spanning the areas of protein detection and viral vector characterization. The first case study details the use of a multi-step signal amplification assay to enable the detection of low abundant molecules, such as cytokines, at lower concentrations than can be detected using the standard one-step binding approach. Cytokines are sandwiched between biotinylated and HRP-conjugated antibodies, then dipped into 3-amino-9-ethylcarbazole (AEC) reagent, resulting in an enhancement of the cytokine detection sensitivity. The second case study uses BLI for the quantitation of mixed populations of a bispecific antibody (bsAb). Bridging and dual binding assay formats are evaluated for their assessment of bsAb antigen binding kinetics and ability to determine the ratio of correctly assembled bsAb within a sample. In the third case study, BLI detection principles are used to estimate the percentage full capsids in a mixed population of AAV particles. Collectively, these case studies demonstrate the versatility of Octet

Indexed as

Biosensing TechniquesInterferometryVirionAntibodies, BispecificCytokinesHumansAntibodies, BispecificCytokinesAAVBiolayer InterferometrybiosensorsbispecificscapsidscytokineskineticsOctet® BLIsensitivity

Identifiers

PMID42505466
PMCPMC13406669

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.